Model-based Design of Adaptive Embedded Systems

The book elucidates the revolutionary and riveting research in the ultramodern domain of drone technologies, drone-enabled IoT applications, and artificial intelligence-based smart surveillance. The book explains the most recent developments in the field, challenges, and future scope of drone technologies. Beyond that, it discusses the importance of a wide range of design applications, drone/UAV development, and drone-enabled smart healthcare systems for smart cities. It describes pioneering work on mitigating cyber security threats by employing intelligent machine learning models in the designing of IoT-aided drones. The book also has a fascinating chapter on application intrusion detection by drones using recurrent neural networks. Other chapters address interdisciplinary fields like artificial intelligence, deep learning, the role of drones in healthcare in smart cities, and the importance of drone technology in agriculture.

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:Thoroughly updated material throughoutNew chapters on boosting, ad

Against the background of often esoteric literature in semiotics, this book offers a fresh and rigorous new interpretation of how to approach the study of communication, signs and meaning. Grounded in a deductive theory of interacting systems, Piotr Sadowski's book provides an accessible account of the hierarchy of communication.Divided into two parts, this book argues in the first section that a deductive semiotic theory generates communication situations of increasing complexity, from contiguous communication to indirect, referential forms based on indexical, iconic, and symbolic signs. Within this system, Sadowski explains how key concepts of the semiotic model such as information, parainformation and metainformation can account for degrees of cognitive complexity of communication processes, including the perception and interpretation of signs on literal and figurative levels. After this clear, step-by-step exposition of the theory of interacting systems, Systemic Semiotics then

Wildland fires are among the most complicated environmental phenomena to model. Fire behavior models are commonly used to predict the direction and rate of spread of wildland fires based on fire history, fuel, and environmental conditions; however, more sophisticated computational fluid dynamic models are now being developed. This quantitative analysis of fire as a fluid dynamic phenomenon embedded in a highly turbulent flow is beginning to reveal the combined interactions of the vegetative structure, combustion-driven convective effects, and atmospheric boundary layer processes. This book provides an overview of the developments in modeling wildland fire dynamics and the key dynamical processes involved. Mathematical and dynamical principles are presented, and the complex phenomena that arise in wildland fire are discussed. Providing a state-of-the-art survey, it is a useful reference for scientists, researchers, and graduate students interested in wildland fire behavior from a broad

Most systems involved in a chemical process plant are interactive multivariable systems, to control which, the transfer function matrix model is required. This lucid book considers the identification and control of such systems. It discusses open loop and closed loop identification methods, as well as the design of multivariable controllers based on steady state gain matrix. Simple methods for designing controllers based on transfer function matrix model have been reviewed. The design of controllers for non-square systems, and closed loop identification of multivariable unstable systems by the optimization method are also covered. Several simulation examples and exercise problems at the end of each chapter further help the reader consolidate the knowledge gained. This book will be useful to any engineering student, researcher or practitioner who works with interactive, multivariable control systems.

This comprehensive text provides an excellent introduction to the state of the art in the identification of network-connected systems. It covers models and methods in detail, includes a case study showing how many of these methods are applied in adaptive optics and addresses open research questions. Specific models covered include generic modelling for MIMO LTI systems, signal flow models of dynamic networks and models of networks of local LTI systems. A variety of different identification methods are discussed, including identification of signal flow dynamics networks, subspace-like identification of multi-dimensional systems and subspace identification of local systems in an NDS. Researchers working in system identification and/or networked systems will appreciate the comprehensive overview provided, and the emphasis on algorithm design will interest those wishing to test the theory on real-life applications. This is the ideal text for researchers and graduate students interested in

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod